Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1222953
www . machinerylubrication.com | March - April 2020 | 11 at a later stage once a person has conducted a more thorough inter- pretation of that data, often with a signif icant time lag between sampling and outcome. I have seen many examples of an asset expe- riencing a catastrophic loss event during this time lag. Secondly, compare data accuracy versus context. Many times, during discussions around some of the new real-time lubricant monitoring tech- nologies/sensors, I am asked about the performance of the solution versus laboratory analysis. is is a great question with a simple answer. What are you looking for? Laborato- ries always will be able to provide a much more detailed analysis of lubri- cants. Sensors and sensing solutions will never fully replace the insight of a lab. e sensor science just does not exist. Labs can offer accuracy levels to multiple decimal places and distinguish between different wear metal particles down to single parts per million. ere will always be a need for this level of accuracy, but you must keep in mind your answer to the original question (what are you looking for?). ere are two clear and distinct paths to this question. On the first path, at the asset level, in real time, during the running of the asset, you primarily need to know three simple things: is everything OK, what's wrong and what should I do? On the second path, after a major failure, forensic analysis should be performed, an autopsy on a failed asset. In this instance, post-failure, many more questions need to be answered, such as why did it fail, what is the root cause and what do we need to address on other assets to prevent their failure? e first path is where the need for new digital technology that drives real-time data comes into play. Data that is "good enough" but provides what you need to know (rather than what you can be told) is much more valuable in real time with context than extremely accurate data taken at a single snapshot in time. Consider again the person's weight analogy. e man wears a device on his wrist that measures and tracks his weight and other health indicators such as blood pres- sure, heart rate, etc. e software on the wearable device can track the data trends and provide context as well as interpreted results based on known boundaries. is data is not as accurate as you would get at a hospital, where large machines would be used to measure each indi- vidual indicator. However, the data is "good enough," it's in real time, it tracks trends and therefore offers context. It has the four elements of good data: analysis (the device takes measurements and converts them into data), interpretation (it puts the relevant data into charts that show what the data means), context (it looks at trends over time and often has context around what activity you were doing at the time), and outcome ML